Clinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific end-to-end models and pipeline models usually suffer from the lack of dataset and error propagation. In this paper, we present a question answering based clinical text structuring (QA-CTS) task to unify different specific tasks and make dataset shareable. A novel model that aims to introduce domain-specific features (e.g., clinical named entity information) into pre-trained language model is also proposed for QA-CTS task. Experimental results on Chinese pathology reports collected from Ruijing Hospital demonstrate our presented QA-CTS task is very effective to improve the performance on specific tasks. Our proposed model also competes favorably with strong baseline models in specific tasks.
!MSA at AraHealthQA 2025 Shared Task: Enhancing LLM Performance for Arabic Clinical Question Answering through Prompt Engineering and Ensemble Learning
Faculty of Computer Science, MSA University（计算机科学学院，MSA大学）
Feasibility of Structuring Stress Documentation Using an Ontology-Guided Large Language Model
College of Nursing, Seoul National University（首尔国立大学护理学院）；The Research Institute of Nursing Science, Seoul National University（首尔国立大学护理科学研究所）；Center of Human-Caring Nurse Leaders for the Future by the Brain Korea (BK 21) four project, College of Nursing, Seoul National University（首尔国立大学护理学院，韩国脑计划（BK21）第四阶段未来人文关怀护理领军人才中心）；College of Medicine, Seoul National University（首尔国立大学医学院）
Clinical trial records are variable resources or the analysis of patients and diseases. Information extraction from free text such as eligibility criteria and summary of results and conclusions in clinical trials would better support computer-based eligibility query formulation and electronic patient screening. Previous research has focused on extracting information from eligibility criteria, with usually a single pair of medical entity and attribute, but seldom considering other kinds of free text with multiple entities, attributes and relations that are more complex for parsing. In this pape…
CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Models in Mental Health Question Answering
Department of Computer Science, University of Southern California（南加州大学计算机科学系）；Department of Electrical and Computer Engineering, University of Southern California（南加州大学电气与计算机工程系）；Suzanne Dworak-Peck School of Social Work, University of Southern California（南加州大学苏兹安·德沃拉克-佩克社会工作学院）；Department of Psychiatry and the Behavioral Sciences, University of Southern California（南加州大学精神病学与行为科学系）；Annenberg School for Communication, University of Southern California（南加州大学安纳伯格通信学院）
Emulating Human Cognitive Processes for Expert-Level Medical Question-Answering with Large Language Models
Dell Medical School at the University of Texas（德克萨斯大学戴尔医学院）；William Beaumont University Hospital（威廉·博蒙特大学医院）；Austin State Hospital（奥斯汀州立医院）；Institute of Mental Health, University of Nottingham（诺丁汉大学精神卫生研究所）；Government Medical College and Hospital, Chandigarh（昌迪加尔政府医学院及医院）；University of Augsburg（奥格斯堡大学）；McGovern Medical School and The University of Texas Health Science Center（麦戈文医学院与德克萨斯大学健康科学中心）；Sheikh Shakhbout Medical City（谢赫·沙赫布特医疗城）；Dr Akbar Niazi Teaching Hospital（阿克巴尔·尼亚兹博士教学医院）；Centro Médico ABC（ABC医疗中心）；Houston Regional Gastroenterology Institute（休斯顿地区胃肠病学研究所）；NeuroReef Labs（NeuroReef实验室）
Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology
Microsoft Research（微软研究院）；Providence Health & Services（普罗维登斯医疗服务机构）；Earle A. Chiles Research Institute（厄尔·A·奇尔斯研究所）；Providence Cancer Institute（普罗维登斯癌症研究所）
Answering real-world clinical questions using large language model based systems
Atropos Health（阿特罗波斯健康公司）；University of California, Los Angeles（加州大学洛杉矶分校）；The Hospital for Sick Children（病童医院）；University of California, San Diego（加州大学圣地亚哥分校）；University of Michigan（密歇根大学）；Columbia University（哥伦比亚大学）；Stanford University（斯坦福大学）
Objective: The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual curation is expensive and time-consuming. Developing natural language processing (NLP) methods for structuring RWD is thus essential for scaling real-world evidence generation. Materials and Methods: Traditional rule-based systems are vulnerable to the prevalent linguistic variations and ambiguities in clinical text, and prior applications of machine-learning methods typically require sentence-level or report-level labeled examples that are har…
Beyond MedQA: Towards Real-world Clinical Decision Making in the Era of LLMs
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Children’s Healthcare of Atlanta（亚特兰大儿童医院）；Nell Hodgson Woodruff School of Nursing, Emory University（埃默里大学奈尔·霍德森·伍德鲁夫护理学院）
Large Language Models in the Clinic: A Comprehensive Benchmark
University of Oxford（牛津大学）；Amazon（亚马逊公司）；Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Institut polytechnique de Paris（巴黎综合理工学院）；Oxford-Suzhou Centre for Advanced Research（牛津-苏州高等研究院）
Objectives: To adapt and evaluate a deep learning language model for answering why-questions based on patient-specific clinical text. Materials and Methods: Bidirectional encoder representations from transformers (BERT) models were trained with varying data sources to perform SQuAD 2.0 style why-question answering (why-QA) on clinical notes. The evaluation focused on: 1) comparing the merits from different training data, 2) error analysis. Results: The best model achieved an accuracy of 0.707 (or 0.760 by partial match). Training toward customization for the clinical language helped increase 6…
Department of Computer Science and Engineering, IIT (BHU)（印度理工学院（BHU）计算机科学与工程系）
Segmenting an unordered text document into different sections is a very useful task in many text processing applications like multiple document summarization, question answering, etc. This paper proposes structuring of an unordered text document based on the keywords in the document. We test our approach on Wikipedia documents using both statistical and predictive methods such as the TextRank algorithm and Google's USE (Universal Sentence Encoder). From our experimental results, we show that the proposed model can effectively structure an unordered document into sections.
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering
MoE Key Lab of Artificial Intelligence（摩尔电子关键实验室）；X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University（X-LANCE实验室，计算机科学学院，上海交通大学）；Jiangsu Key Lab of Language Computing（江苏语言计算重点实验室）；AISpeech Co., Ltd.（AISpeech公司）；Suzhou Laboratory（苏州实验室）
Stevens Institute of Technology（史蒂文斯理工学院）；Virginia Tech（弗吉尼亚理工大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
Question Answering (QA) in clinical notes has gained a lot of attention in the past few years. Existing machine reading comprehension approaches in clinical domain can only handle questions about a single block of clinical texts and fail to retrieve information about multiple patients and their clinical notes. To handle more complex questions, we aim at creating knowledge base from clinical notes to link different patients and clinical notes, and performing knowledge base question answering (KBQA). Based on the expert annotations available in the n2c2 dataset, we first created the ClinicalKBQA…
The Ohio State University（俄亥俄州立大学）；University of Michigan（密歇根大学）；George Mason University（乔治梅森大学）
Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts. Studies show that neural QA models trained on one corpus may not generalize well to new clinical texts from a different institute or a different patient group, where large-scale QA pairs are not readily available for model retraining. To address this challenge, we propose a simple yet effective framework, CliniQG4QA, which leverages question generation (QG) to synthesize QA pairs on new clinical contexts and boosts QA models without requiring manual annotations. In order …
Institute for Infocomm Research (I2R), A*STAR（新加坡科技研究局资讯通信研究院）
There is growing interest in the automated extraction of relevant information from clinical dialogues. However, it is difficult to collect and construct large annotated resources for clinical dialogue tasks. Recent developments in natural language processing suggest that large-scale pre-trained language backbones could be leveraged for such machine comprehension and information extraction tasks. Yet, due to the gap between pre-training and downstream clinical domains, it remains challenging to exploit the generic backbones for domain-specific applications. Therefore, in this work, we propose a…
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）
The recent developments in the field of biomedicine have made large volumes of biomedical literature available to the medical practitioners. Due to the large size and lack of efficient searching strategies, medical practitioners struggle to obtain necessary information available in the biomedical literature. Moreover, the most sophisticated search engines of age are not intelligent enough to interpret the clinicians' questions. These facts reflect the urgent need of an information retrieval system that accepts the queries from medical practitioners' in natural language and returns the answers …
School of Biomedical Informatics（生物医学信息学院）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
Clinical semantic parsing (SP) is an important step toward identifying the exact information need (as a machine-understandable logical form) from a natural language query aimed at retrieving information from electronic health records (EHRs). Current approaches to clinical SP are largely based on traditional machine learning and require hand-building a lexicon. The recent advancements in neural SP show a promise for building a robust and flexible semantic parser without much human effort. Thus, in this paper, we aim to systematically assess the performance of two such neural SP models for EHR q…
Reading and understanding text is one important component in computer aided diagnosis in clinical medicine, also being a major research problem in the field of NLP. In this work, we introduce a question-answering task called MedQA to study answering questions in clinical medicine using knowledge in a large-scale document collection. The aim of MedQA is to answer real-world questions with large-scale reading comprehension. We propose our solution SeaReader--a modular end-to-end reading comprehension model based on LSTM networks and dual-path attention architecture. The novel dual-path attention…
Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning?
University of Delhi（德里大学）；Jamia Hamdard（贾迈亚哈马尔德大学）；Cornell University（康奈尔大学）；Expedia Group（Expedia集团）；DSEU-Okhla；Center for SDGC（SDGC中心）；Stanford University（斯坦福大学）
LongHealth: A Question Answering Benchmark with Long Clinical Documents
Technical University of Munich（慕尼黑工业大学）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；University Hospital Aachen（亚琛大学医院）；Kaduceo；Berliner Hochschule für Technik (BHT)（柏林工业大学）；Mass General Brigham, Harvard Medical School（麻省总医院布莱根医疗集团，哈佛医学院）；Maastricht University（马斯特里赫特大学）；University Hospital RWTH Aachen（亚琛工业大学医院）；Technical University Dresden（德累斯顿工业大学）；German Heart Center Munich（慕尼黑德国心脏中心）
In this work, our aim is to provide a structured answer in natural language to a complex information need. Particularly, we envision using generative models from the perspective of data-to-text generation. We propose the use of a content selection and planning pipeline which aims at structuring the answer by generating intermediate plans. The experimental evaluation is performed using the TREC Complex Answer Retrieval (CAR) dataset. We evaluate both the generated answer and its corresponding structure and show the effectiveness of planning-based models in comparison to a text-to-text model.
EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries
KAIST（韩国科学技术院）；Seoul National University（首尔大学）；Seoul National University Bundang Hospital（首尔大学盆唐医院）；SAIHST, Sungkyunkwan University（成均馆大学）；Yonsei University College of Medicine（延世大学医学院）；Gangnam Severance Hospital（江南塞弗伦斯医院）；Severance Hospital（塞弗伦斯医院）；Seoul Medical Center（首尔医疗中心）；Seoul National University Hospital（首尔大学医院）；National Cancer Center（国立癌症中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Samsung Medical Center（三星医疗中心）
